ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Policy Library Redundancy Analysis Using K-means Clustering.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This capstone project investigates the application of artificial intelligence (AI) techniques, specifically sentence embedding and k-means clustering using large language models, to address the challenge of policy library redundancy within a healthcare setting. The project aimed to demonstrate the viability of using AI-assisted tools in policy library management, targeting a 5% reduction in the overall policy library at a large academic healthcare system. By collaborating with the accreditation team and developing a Python-script prototype, the study showed that AI-assisted methods could significantly enhance efficiency and reduce labor in policy library management. Results indicate a potential 4% reduction in library size, underscoring the method's effectiveness and the opportunity for further optimization. This research contributes to the emerging field of AI in healthcare administration, offering a scalable model for improving policy library management processes in various healthcare contexts.
Indexed as
Identifiers
40417516PMC12099442What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.